Predicting Rainfall Based On Historical Data
Download Predicting Rainfall Based On Historical Data PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Predicting Rainfall Based On Historical Data book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages.
Applications of Machine Learning in Hydroclimatology
Applications of Machine Learning in Hydroclimatology is a comprehensive exploration of the transformative potential of machine learning for addressing critical challenges in water resources management. The book explores how artificial intelligence can unravel the complexities of hydrological systems, providing researchers and practitioners with cutting-edge tools to model, predict, and manage these systems with greater precision and effectiveness. It thoroughly examines the modeling of hydrometeorological extremes, such as floods and droughts, which are becoming increasingly difficult to predict due to climate change. By leveraging AI-driven methods to forecast these extremes, the book offers innovative approaches that enhance predictive accuracy. It emphasizes the importance of analyzing non-stationarity and uncertainty in a rapidly evolving climate landscape, illustrating how statistical and frequency analyses can improve hydrological forecasts. Moreover, the book explores the impact of climate change on flood risks, drought occurrences, and reservoir operations, providing insights into how these phenomena affect water resource management. To provide practical solutions, the book includes case studies that showcase effective mitigation measures for water-related challenges. These examples highlight the use of machine learning techniques such as deep learning, reinforcement learning, and statistical downscaling in real-world scenarios. They demonstrate how artificial intelligence can optimize decision-making and resource management while improving our understanding of complex hydrological phenomena. By utilizing machine learning architectures tailored to hydrology, the book presents physics-guided models, data-driven techniques, and hybrid approaches that can be used to address water management issues. Ultimately, Applications of Machine Learning in Hydroclimatology empowers researchers, practitioners, and policymakers to harness machine learning for sustainable water management. It bridges the gap between advanced AI technologies and hydrological science, offering innovative solutions to tackle today's most pressing challenges in water resources.
Proceedings of 5th International Conference on Artificial Intelligence and Smart Energy
This book discusses the latest developments in computing techniques that power smart energy and sustainable solutions. Over the last few years, artificial intelligence (AI) has been more deeply embedded in our lives, revolutionizing industries and communication. Intelligent computing models are now transforming traditional energy applications in this digital age through smart automation, optimization, and adaptation. The book addresses major facets of intelligent computing and communication technologies, such as intelligent data analysis, predictive modeling, optimization, neural networks, AI, machine learning, deep learning, and the Internet of Things (IoT). All these technologies are discussed in practical applications, e.g., smart cities and smart industries, their transformative possibilities.
Proceedings of the 12th International Conference on Soft Computing for Problem Solving
This book provides an insight into 12th International Conference on Soft Computing for Problem Solving (SocProS 2023), organized by The Department of Applied Mathematics and Scientific Computing, Saharanpur Campus of Indian Institute of Technology, Roorkee, India, in conjunction with Continuing Education Center during 11–13 August 2023. This book presents the latest achievements and innovations in the interdisciplinary areas of soft computing, machine learning, and data science. It covers original research papers in the areas of algorithms (artificial neural network, deep learning, statistical methods, genetic algorithm, and particle swarm optimization) and applications (data mining and clustering, computer vision, medical and health care, finance, data envelopment analysis, business, and forecasting applications). This book is beneficial for young as well as experienced researchers dealing across complex and intricate real-world problems for which finding a solution by traditional methods is a difficult task.